Health & Environmental Research Online (HERO)


Print Feedback Export to File
196821 
Journal Article 
Review 
The influence of genetic polymorphisms on population variability in six xenobiotic-metabolizing enzymes 
Ginsberg, G; Smolenski, S; Neafsey, P; Hattis, D; Walker, K; Guyton, KZ; Johns, DO; Sonawane, B 
2009 
Journal of Toxicology and Environmental Health, Part B: Critical Reviews
ISSN: 1093-7404
EISSN: 1521-6950 
12 
307-333 
English 
This review provides variability statistics for polymorphic enzymes that are involved in the metabolism of xenobiotics. Six enzymes were evaluated: cytochrome P-450 (CYP) 2D6, CYP2E1, aldehyde dehydrogenase-2 (ALDH2), paraoxonase (PON1), glutathione transferases (GSTM1, GSTT1, and GSTP1), and N-acetyltransferases (NAT1 and NAT2). The polymorphisms were characterized with respect to (1) number and type of variants, (2) effects of polymorphisms on enzyme function, and (3) frequency of genotypes within specified human populations. This information was incorporated into Monte Carlo simulations to predict the population distribution and describe interindividual variability in enzyme activity. The results were assessed in terms of (1) role of these enzymes in toxicant activation and clearance, (2) molecular epidemiology evidence of health risk, and (3) comparing enzyme variability to that commonly assumed for pharmacokinetics. Overall, the Monte Carlo simulations indicated a large degree of interindividual variability in enzyme function, in some cases characterized by multimodal distributions. This study illustrates that polymorphic metabolizing systems are potentially important sources of pharmacokinetic variability, but there are a number of other factors including blood flow to liver and compensating pathways for clearance that affect how a specific polymorphism will alter internal dose and toxicity. This is best evaluated with the aid of physiologically based pharmacokinetic (PBPK) modeling. The population distribution of enzyme activity presented in this series of articles serves as inputs to such PBPK modeling analyses.